Evidence map›Paper›PMID 39706923›Full record

ArticleEuropean radiology2025

Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.

Jie Lee, Jhen-Bin Lin, Wan-Chun Lin, Ya-Ting Jan, Yi-Shing Leu, Yu-Jen Chen, Kun-Pin Wu

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Article in European radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Machine learning for predicting muscle loss after radiotherapy using clinical and toxicity data in oral cavity cancer.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026
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  2. Review
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  4. Predictors of aspiration pneumonia in oral cavity cancer patients undergoing radiation therapy: A focus on skeletal muscle index.Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer · 2026
    Article
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4 · The record

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5 · Who and what money

Authors and funding

7 authors.

Jie LeeDepartment of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan. sinus.5706@mmh.org.tw.ORCID http://orcid.org/0000-0003-1445-2393
Jhen-Bin LinDepartment of Radiation Oncology, Changhua Christian Hospital, Changhua, Taiwan.
Wan-Chun LinInstitute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan.
Ya-Ting JanDepartment of Radiology, MacKay Memorial Hospital, Taipei, Taiwan.
Yi-Shing LeuDepartment of Otolaryngology and Head Neck Surgery, MacKay Memorial Hospital, Taipei, Taiwan.
Yu-Jen ChenDepartment of Radiation Oncology, MacKay Memorial Hospital, Taipei, Taiwan.
Kun-Pin WuInstitute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei, Taiwan. kpwu@nycu.edu.tw.

Funding

Mackay Memorial Hospital MMH-113-109Mackay Memorial Hospital MMH-113-53National Science and Technology Council MOST 110-2314-B-195-033National Science and Technology Council MOST 110-2314-B-A49A-506-MY3National Science and Technology Council NSTC 113-2221-E-A49-167-MY3National Science and Technology Council NSTC 113-2314-B-195-011-MY3
6 · The paper itself

Abstract

objectivesMuscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial intelligence to identify the threshold of muscle loss associated with survival in OCSCC. MATERIALS AND

methodsWe enrolled 1087 patients with OCSCC treated with surgery and adjuvant radiotherapy at two tertiary centers (660 in the derivation cohort and 427 in the external validation cohort). Skeletal muscle index (SMI) was measured using pre- and post-radiotherapy computed tomography (CT) at the C3 vertebral level. Random forest (RF), eXtreme Gradient Boosting (XGBoost), and Categorical Boosting (CatBoost) models were developed to predict all-cause mortality, and their performances were evaluated using the area under the curve (AUC). Muscle loss threshold was identified using the SHapley Additive exPlanations (SHAP) method and validated using Cox regression analysis.

resultsIn the external validation cohort, the RF, XGBoost, and CatBoost models achieved favorable performance in predicting all-cause mortality (AUC: 0.898, 0.859, and 0.842). The SHAP method demonstrated that SMI change after radiotherapy was the most important feature for predicting all-cause mortality and consistently identified SMI loss ≥ 4.2% as the threshold in all three models. In multivariable analysis, SMI loss ≥ 4.2% was independently associated with increased all-cause mortality risk in both cohorts (derivation cohort: hazard ratio: 6.66, p < 0.001; external validation cohort: hazard ratio: 8.46, p < 0.001).

conclusionThis study can assist clinicians in identifying patients with considerable muscle loss after treatment and guide interventions to improve muscle mass. KEY POINTS: Question Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity cancer; however, the threshold of muscle loss remains unclear. Findings Explainable artificial intelligence identified muscle loss ≥ 4.2% as the threshold of increased all-cause mortality risk in both derivation and external validation cohorts. Clinical Relevance Muscle loss ≥ 4.2% may be the optimal threshold for survival in patients who receive adjuvant radiotherapy for oral cavity cancer. This threshold can guide clinicians in improving muscle mass after radiotherapy.

Indexed as

Carcinoma, Squamous CellMachine LearningMouth NeoplasmsMuscle, SkeletalSarcopeniaTomography, X-Ray ComputedAdultAgedFemaleHumansMaleMiddle AgedRadiotherapy, AdjuvantRetrospective StudiesComputed tomographyExplainable artificial intelligenceMuscle lossOral cavity cancerRadiotherapy

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.